Method and arrangement in a communication network

ABSTRACT

A method and apparatus for providing labelling information to a third party regarding terminal users in a communication network. A labelling unit receives communication related data generated from executed communications of the terminal users, and fetches stored labelling rules which have been configured specifically for the third party. The labelling unit then converts the communication related data into labelling information, where a communication habits vector is determined by applying the fetched labelling rules on the received communication related data, and the labelling information is determined for the terminal user(s) based on the resulting communication habits vector. The determined labelling information is finally delivered to the third party.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a 35 U.S.C. §371 national stage application of PCTInternational Application No. PCT/SE2009/050821, filed on 26 Jun. 2009,the disclosure and content of which is incorporated by reference hereinas if set forth in its entirety.

FIELD

The invention relates generally to a method and arrangement forproviding information on terminal users in a communication network to areceiving third party in an efficient and comprehensible manner.

BACKGROUND

With the emergence of new communication techniques, different types ofmobile and fixed terminals capable of multimedia communication have beendeveloped for enabling users to consume multimedia services. Newservices involving communication of various types of media, are alsoconstantly being developed for terminal users to increase the field ofusage for their communication terminals. In the following text, the term“user services” generally represents any type of services that can beactivated for a user of a communication terminal. User services are thussomehow related to the user, e.g. services depending on the user'sgeographic position, terminal type, use of address lists, and so forth.

Recently, solutions have been devised for creating and offering relevantand potentially attractive services that have been adapted to differentservice consumers according to their interests and needs in differentsituations. These user services can thus be customised for individualusers depending on their user profiles and/or current situation. Someexamples are advertisements and personalised TV. Solutions have alsobeen suggested for managing groups or “clusters” of users with similarbehaviours, and for adapting various user services to the commoncharacteristics of these user groups. WO 06/115442 (Ericsson) disclosesa mechanism where the particular needs of a user group can be met byproviding relevant context information that has been adapted toparticular interests and needs of the group.

Differentiated adaptation of services for users and user groups maydepend on and require that information on the users' profile, currentsituation, as well as earlier behaviour and habits, is available to theservice providers in a useful manner. This kind of information can beextracted from different sources, typically traffic data available incommunication networks, i.e. information on executed calls and othersessions such as SMS (Short Message Service), MMS (Multimedia MessageService), IMS sessions, and so forth, using various data miningtechniques which have been developed recently. For example, so-called“Machine Learning” (ML) algorithms and tools can be used for theextraction of relevant and useful information on the users from theavailable traffic data, which may be utilised by operators whendeveloping and introducing new services.

Great amounts of traffic data are thus generally available from ChargingData Records (CDR) which are usually generated and stored for thenetworks to support charging for executed calls and sessions. Trafficdata can also be obtained by means of various traffic analysing devices,such as Deep Packet Inspection (DPI) analysers, which can be installedat communication nodes in the network. Further, the CDR data may begenerated from DPI data in some cases. The traffic may involve variouscommunications services that can be detected in this way, such as voice,SMS, MMS, peer-to-peer services, downloading, streaming, and so forth.

A Data Mining Engine (DME) may further be employed that collects trafficdata and extracts user information therefrom using various data miningand machine learning algorithms. The DME may even be used to obtaininformation on the social relations between different users, and eventhe “strength” of those relations, depending on the amount and type ofcommunications these users have conducted with each other as well astime of day, duration and location when making their calls and sessions.

However, the above-described data mining and machine learning algorithmstypically provide rather “raw” output data which can be difficult tointerpret and understand for different receiving parties such as serviceproviders or the like, either external or internal. The traffic data mayalso originate from different communication techniques producingdifferent outcomes from the algorithms above. In addition, no usefuluniversal and consistent “language” has yet been defined and establishedto describe, e.g., different types of service usage and social relationsin a uniform or standardised manner understandable for any datareceiving parties. As a result, the output data from a DME of today maywell be interpreted differently by different receiving parties, and/ormay not even be properly understood at all or interpreted inaccurately.

The DME data is also often presented in communication technologyspecific terms requiring special knowledge to understand. It istherefore not unusual to employ experts skilled in data mining andcommunication techniques in order to interpret, process and describe theDME output data correctly. These persons should thus be veryaccomplished in interpreting data mining results as well as inbehavioural science, among other things. Employing such experts may becostly or not even possible. Still further, different experts maydescribe the DME output data in different ways with inconsistent terms.

FIG. 1 illustrates an example of how data mining can be employed for acommunication network, according to the prior art. A data mining engine(DME) 100 comprises various machine learning algorithms (MLA:s) 100 awhich are used for processing traffic data (TD) provided from aso-called “Mining Object Repository” (MOR) 102. The MOR 102 collects CDRinformation and DPI information, either intermittently or on a morecontinuous basis, from the network in the manner described above. Afterprocessing the traffic data, the DME 100 provides raw output data to aplurality of third parties 104 (A,B,C . . . ), e.g. different serviceproviders, and such DME data may be difficult to interpret and use, asexplained above. It is thus up to the receiving third parties 104 howthe output data from the DME 100 is interpreted and used, e.g. in termsof user profiles, social networks, user segments, and so forth, whichmay require considerable efforts and skills.

SUMMARY

It is an object of the present invention to address at least some of theissues outlined above. It is thus an object to provide useful andintelligible information on terminal users in the network, based ontheir communication habits and service usage. These objects and otherscan be achieved primarily by a solution according to the appendedindependent claims.

According to different aspects, a method and an apparatus are definedfor providing labelling information to a receiving third party regardingone or more terminal users in a communication network, by means of alabelling unit connected to a data mining system.

In the inventive method, communication related data is received whichhas been generated from executed communications of the one or moreterminal users. Further, labelling rules which have been configured andstored in a storage specifically for the third party, are fetched fromthe storage. The received communication related data is then convertedinto labelling information, wherein a communication habits vector isdetermined by applying the fetched labelling rules on the receivedcommunication related data, and the labelling information is determinedfor the terminal user(s) based on the resulting communication habitsvector. The labelling information represents a description of theterminal user(s) with respect to their communication habits. Thedetermined labelling information can then be delivered to the thirdparty.

The inventive labelling unit comprises a receiving unit adapted toreceive communication related data generated from executedcommunications of the one or more terminal users. The labelling unitfurther comprises a converting unit adapted to fetch stored labellingrules which have been configured specifically for the third party, andto convert the received communication related data into labellinginformation, including determining values of parameters in acommunication habits vector by applying the fetched labelling rules onthe received communication related data, and determining labellinginformation for the terminal user(s) based on the communication habitsvector. The labelling information represents a description of theterminal user(s) with respect to their communication habits. Thelabelling unit also comprises a delivery unit adapted to deliver thedetermined labelling information to the third party.

Using the inventive method and labelling unit above will enable networkoperators to deliver intelligible information of end users, extractedvia machine learning functions, to third parties in a more efficient andconsistent way and automatically. The need for data mining experts canalso be minimized, as they are only needed when configuring the customerspecific labelling rules.

The invented method and labelling unit may be implemented according toany of the following optional embodiments.

In one embodiment, the delivered labelling information includes a label,category or class of the terminal user(s) as defined by the labellingrules. The labelling information may also be described with aterminology independent on the underlying traffic types andcommunication techniques.

In another embodiment, the delivery unit delivers the labellinginformation using a protocol and an interface adapted to the thirdparty. In yet another embodiment, the labelling rules are configured ina storage unit by defining the communication habits vector as aplurality of measurable communication habits parameters, and configuringparameter thresholds or intervals as limits for predefined user labels,classes or categories.

In further possible embodiments, the converting unit determines thecommunication habits vector by determining the values of thecommunication habits parameters from the received communication relateddata, and determines a user label, class or category based on thepreconfigured limits for each parameter in the communication habitsvector. The communication habits vector may be representative for asingle terminal user or a cluster of plural terminal users havingsimilar communication habits.

In yet another embodiment, the receiving unit receives the communicationrelated data from a DME (Data Mining Engine) as processed by one or moreMLA:s (Machine Learning Algorithms).

In further embodiments, the converting unit includes one or more of: asocial network module adapted to create labelling information relatingto social network relations of the users, a profile module adapted togenerate a profile of a user by customising the user profile expressedin a format used by a DME from which the communication related data isreceived, and a cluster module adapted to generate a profile of acluster of users by customising the cluster profile expressed in theformat used by the DME.

Further preferred features and benefits of the present invention willbecome apparent from the detailed description below.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described in more detail by means of preferredembodiments and with reference to the accompanying drawings, in which:

FIG. 1 is a schematic block diagram illustrating data mining in acommunication network, according to the prior art.

FIG. 2 is a schematic block diagram illustrating a procedure forproviding labelling information to a receiving third party regardingterminal users in the network, according to one embodiment.

FIG. 3 is a flow chart illustrating a procedure for providing labellinginformation, according to another embodiment.

FIG. 4 is a schematic diagram illustrating an example of how acommunication habits vector can be determined in the case of threedifferent communication habits parameters, according to furtherembodiments.

FIG. 5 is a schematic block diagram illustrating a labelling unit inmore detail, according to further embodiments.

FIG. 6 is a schematic block diagram illustrating a converting unit inthe labelling unit in more detail, according to further embodiments.

DETAILED DESCRIPTION

In this description, the term “labelling information” is used torepresent a behaviour description of terminal users that can be tracedfrom their communication habits and service usage. Briefly described,the invention provides a solution that enables creation of labellinginformation for terminal users, for delivery to third parties in anintelligible format which has been individually adapted or customisedfor each receiving third party according to predefined labelling rules.In this solution, the labelling information can be regarded as“customised” for each receiving third party, even though it is alsopossible that more than one third party can receive the same type oflabelling information.

The labelling information thus basically characterises the users in acomprehensible manner and could also be referred to as classification,categorisation, cataloguing or sorting of users as related to theircommunication habits and usage of communication services. In thisdescription, “communication habits” basically refer to the usage ofcommunication services, but should further be understood in a broadsense, i.e. any user behaviour or current circumstances when makingcalls and sessions, e.g. the current geographic position, time of day,duration, type of terminal used, associated address lists, use ofterminal functions, and so forth.

The labelling information is created in a novel node or functionreferred to as a “labelling unit”, using communication related datareceived from a DME or the like. The term “communication related data”is used here to represent any data that can be obtained by means ofconventional data mining services, which may include raw traffic data aswell as more refined information about users, social networks, clustersand user profiles derived by analyzing the traffic data.

The labelling information describes the terminal users with respect totheir communication habits and service usage which can be derived byinterpreting and analysing the received communication related data. Thelabelling information may be expressed as user labels, categories orsimilar descriptive terms, e.g. referring to behaviours and socialrelations with other users. Any descriptive terms may be used as thelabelling information as stipulated by the predefined labelling rules.

In this description, a “third party” could be any party that is entitledto receive such labelling information, e.g. service or contentproviders, network operators and vendors, as well as the currentoperator's own analysing department or the like. The labellinginformation can be used by the third party to create or adapt servicesand products that may be provided or offered to the users, although theinvention is not limited to any particular use of the labellinginformation by the third party.

Labelling rules are first defined for a specific third party in aconfiguring or preparation phase, which may require expert knowledge ofpersons skilled in data mining and/or communication techniques. Thelabelling rules can be freely defined and customised for individualthird parties, even though default rules may also be selected, toconvert, or “translate”, the communication related data into labellinginformation according to the needs and capabilities of each receivingthird party.

In an execution phase, the labelling rules are applied on communicationrelated data supplied from a DME or other similar data sources, forexecuting the above third party adapted translation. In more detail, alogical “communication habits vector” is determined for one or moreterminal users from the received communication related data. Thecommunication habits vector is defined by a plurality of measurablecommunication habits parameters which reflect different aspects ofcommunication habits in technical terms.

Some examples of communication habits parameters that can be measuredare: 1) number of executed voice calls, 2) average duration of executedvoice calls, 3) number of sent or received SMS:s, 4) amount of Internetsessions during night-time, 5) number of sessions made within apredefined area, and so forth. It can be easily understood that manydifferent types of communication habits parameters can be selected fordefining the communication habits vector, and the invention is notlimited in this respect. An example of determining such vectors forcreating labelling information for users will be described in moredetail below with reference to FIG. 4.

In the labelling unit, the labelling information is determined for theterminal user(s) based on the determined communication habits vector.Each communication habits parameter in the communication habits vectorcan have different predetermined thresholds which dictate the resultinglabelling information. Thus, when the value of a measured communicationhabits parameter for a user or user group exceeds a predeterminedthreshold, or is within a predetermined interval between two thresholds,a certain labelling information is implied. For example, if the numberof executed voice calls exceeds a certain threshold and the average callduration also exceeds another threshold for a user, that user may belabelled “busy speaker”. The labelling information may also be expressedas a rating of some user feature, e.g. “Speaking habits” may be rated1-10 where 1 implies a very sparse speaker and 10 implies a very busyspeaker.

Finally, the determined labelling information is delivered to the thirdparty. In this way, the delivered labelling information will have awell-known significance and meaning to the receiving third party. Itshould be noted that any expert knowledge will basically be requiredonly once, i.e. when defining the labelling rules, but not during theexecution phase for interpreting the DME data as in the previously knownsolutions, which can be a significant advantage.

An exemplary procedure will now be described with reference to FIG. 2,for providing labelling information to a receiving third party regardingone or more terminal users in a communication network. In the figure, alabelling unit 200 is shown which may be controlled by an operator ofthe network or by a separate party providing the labelling informationto any third party entitled to receive the labelling information. Thelabelling unit 200 comprises a plurality of storage units 202 forholding labelling rules of different individual third parties A, B, C, .. . , a data converter 204 for converting communication related datainto customised labelling information, and a plurality of interfaceunits 206 configured for delivering the labelling information to thethird parties A, B, C, . . . . In this description, the storage units202 are treated logically as separate units, although in practice theymay be implemented in a common database or the like.

In a first shown step 2:1, labelling rules are configured in the storageunits 202 specifically for the individual third parties A, B, C, . . . ,which is done independently for each third party. This step is executedfor each individual third party as controlled by that party.

Configuring labelling rules includes defining a communication habitsvector by a plurality of communication habits parameters, and alsoconfiguring parameter thresholds or intervals as limits for differentuser labels, classes or categories. Any number of such parameters may beselected for defining the communication habits vector, and the vectorshould be understood as purely logical even though it can be visualisedas a spatial vector in the case of 1-3 parameters, which will be madebelow when describing FIG. 4. In practice, the communication habitsvector may typically be defined with 1-10 different communication habitsparameters and corresponding limits or thresholds, although withoutlimiting the invention in this respect.

Configuring labelling rules further includes defining the labellinginformation in terms that are comprehensible to the third party, i.e.the above user labels, classes or categories. The labelling informationis preferably described with a terminology independent on the underlyingtraffic types and communication techniques.

The first step 2:1 is thus made in a preparation phase of the proceduree.g. when setting up the labelling unit 200 and/or whenever a new thirdparty is added, or when some modification is desired in theconfiguration of any third party. A next step 2:2 illustrates that a DME208 comprising various MLA:s 208 a collects traffic data generated bycommunication activities of users 210 in a network. The DME 208 alsoprovides communication related data to the labelling unit 200 which isthen received by the data converter 204 in a step 2:3 for translationinto customised labelling information. Steps 2:2 and 2:3 can be executedindependently of each other and more or less continuously. However, thesupply of communication related data to the labelling unit 200 may alsobe done at certain intervals according to a predetermined scheme or ondemand from the third party.

In this example, labelling information is to be determined and deliveredto a specific third party C, although the same procedure could beexecuted for any one or more of the third parties A, B, C, . . . . Whenreceiving the communication related data, the data converter 204 thusfetches the labelling rules that were preconfigured for third party Cfrom the storage unit of C, in a next step 2:4.

The data converter 204 then performs a conversion by translating thereceived communication related data according to the fetched rules, in astep 2:5, into labelling information. The conversion includes a firstoperation of determining parameter values in the communication habitsvector, and a second operation of determining a user label, class orcategory that corresponds to the resulting communication habits vectoras determined by the preconfigured limits for each parameter. It is alsopossible to present the parameter values as such as the labellinginformation, thereby basically omitting the second operation above.Hence, the determined user label, class or category is then delivered aslabelling information to third party C over the interface of C, as shownin a final step 2:6.

Another exemplary procedure will now be described with reference to theflow chart in FIG. 3. The procedure comprises steps executed by alabelling unit connected to a data mining system, basically acting asthe labelling unit 200 in FIG. 2, for providing customised labellinginformation to a receiving third party regarding one or more terminalusers in a communication network. It is assumed that labelling rulesthat have been preconfigured in the labelling unit for the third partybasically as described for step 2:1 above. It should be noted that, eventhough only one third party is described here, the following may bevalid for delivering customised labelling information to any number ofthird parties.

In a first step 300, communication related data is received from a DMEor the like which has been analysed, or “data mined”, by the DME,basically corresponding to step 2:3 above. The DME has thus generatedthe communication related data from communications executed by theterminal user(s), basically in the manner described above. In a nextstep 302, the labelling rules that have been preconfigured in thelabelling unit specifically for the third party are fetched from therule storage, basically corresponding to step 2:4 above.

Then, the communication related data received in step 300 is convertedinto labelling information in the following steps 304 and 306. In moredetail, step 304 illustrates that parameter values in a communicationhabits vector are determined, or measured, by applying the fetchedlabelling rules on the received communication related data. As saidabove, the term communication habits vector is used in a logical senseand implies the measured values of a set of communication habitsparameters reflecting different aspects of the users' communicationhabits.

The next step 306 illustrates that the customised labelling informationis then determined for the terminal user(s) based on the communicationhabits vector, where the labelling information represents a descriptionof the terminal user(s) with respect to their communication habits. Asalso said above, the customised labelling information may be defined inany manner as controlled by the third party according to the configuredrules, e.g. in terms of the measured parameters as such or in morerefined descriptive terms, without limitation to the invention. Finally,the determined labelling information is delivered to the third party ina further step 308.

As mentioned above, the communication habits vector is defined by anumber of selected and predefined measurable communication habitsparameters that can be measured for the users by means of the receivedcommunication related data. Some examples of communication habitsparameters were also briefly mentioned above. The number and type ofparameters can be freely configured in the labelling rules for eachthird party.

FIG. 4 illustrates schematically one possible example of depicting suchcommunication habits vectors in a logical multi-dimensional diagram fordifferent users, in the case of having three communication habitsparameters. Each selected and relevant parameter may thus correspond toa dimension, that is to say in a logical sense, and the communicationhabits vector will therefore be defined by values of each relevantcommunication habits parameter.

The three communication habits parameters P(x), P(y) and P(z) thus forma logical three-dimensional space in this example, which is depicted inthe figure as a logical 3-D parameter diagram. However, any number ofparameters, or “dimensions”, is possible without limitation to theinvention. Parameter values have been collected for a plurality of usersA, B, C, . . . and each user can therefore be represented in the diagramas a vector or 3-D projection where entity A has values P(x)_(A),P(y)_(A), P(z)_(A), and so forth. It should be noted that the dimensionsin this diagram are abstract or logic representations of thecommunication habits parameters, and not physical dimensions even thougha communication habits parameter as such may relate to the geographicalposition of a user. A similar logical communication habitsrepresentation can also be made for groups of users having similarcommunication habits.

The diagram thus shows the users A, B, C, . . . at different spots inthe 3-D projection and the parameter values in those spots define theircommunication habits vectors. Further, one or more maximum limits havebeen defined for each parameter as dictating the conditions for aparticular label, category or class, which is shown as a label border400 in this case. A similar label border can also be defined for one ormore minimum parameter limits as well, not shown.

The label border is illustrated logically as a regular sphere in thisexample, although it can have any shape or contour in such a logicalcommunication habits diagram depending on how the label conditions havebeen defined. A communication habits representative for the label maythen effectively constitute a centroid or the equivalent, illustrated as“M” in the figure, representing any entities that fall inside the labelborder 400, thereby qualifying for the label. The communication habitsrepresentative M may be useful for describing any user falling insideand qualifying for the label, e.g. a cluster of users with similarcommunication habits.

In the situation shown in FIG. 4, the parameter values of differentusers A-G are currently located inside the label border 400, therebyqualifying those users to be characterised by the label, although usersmay move in the diagram over time due to changing communication habitsparameter values, e.g. as determined in the steps 2:5 and 304 describedabove. In the example of FIG. 4, the communication habits vector of auser G is moving outside the label border 400 and will therefore bedeemed unqualified, while the vector of a user H is moving inside thelabel border 400 to qualify user H for the label once inside the border400. It should be understood that communication habits vectors can bedetermined for users in an equivalent manner also when more than 3parameters are included.

An exemplary labelling unit for providing labelling information to areceiving third party 504 regarding one or more terminal users in acommunication network, will now be described in more detail withreference to the block diagram in FIG. 5, basically having thefunctionality described for FIG. 2 and FIG. 3. The labelling unit 500can be implemented in or be connected to a data mining system which alsoincludes a DME 502, i.e. the equivalent of DME 208 in FIG. 2, thatprovides communication related data, denoted “CD” in this figure,generated from executed communications of the terminal user(s), e.g.based on CDR information.

The labelling unit 500 comprises a receiving unit 500 b adapted toreceive the communication related data CD from DME 502, e.g. atpredetermined intervals or on a more or less continuous basis or ondemand. The labelling unit 500 also comprises a converting unit 500 cadapted to fetch labelling rules from a storage unit 500 a configuredspecifically for the third party, and to convert the receivedcommunication related data into labelling information as follows.

The data conversion executed by the converting unit 500 c includes afirst operation of determining values of parameters in a communicationhabits vector by applying the fetched labelling rules on the receivedcommunication related data. The data conversion may further include asecond operation of determining labelling information for the terminaluser(s) based on the communication habits vector, the labellinginformation representing a description of the terminal user(s) withrespect to their communication habits. As said above, the labellinginformation may be determined to be the parameter values as is, or amore refined description or translation thereof.

The labelling unit 500 also comprises a delivery unit 500 d adapted todeliver the determined labelling information “LI” to the third party504, e.g. using a specifically adapted communication interface as shownin FIG. 2 at 206.

The invention as exemplified by the above-described embodiments, can beused for various different purposes. For example, the process ofobtaining information on the social relations between different users,from their communication habits and other sources such as address books,can be facilitated. Furthermore, the converting unit 500 c may beconfigured with different modules for different functions depending onwhat type of labelling information is wanted according to the labellingrules of the third parties.

FIG. 6 illustrates an exemplary converting unit 600 having threefunctional modules including a social network module 600 a adapted tocreate labelling information LI(a) relating to social network relationsof the users. The labelling feature in the social network module 600 amay contain algorithms for translating the communication related datainto labels describing various social aspects, e.g. relating to the typeand/or strength of social relations such as “friend”, “acquaintance”,“business partner” etc. When analysing the social aspects, combinationsof geographical location, time of day, and so forth, may also beconsidered. However, this type of information may require further inputfrom other sources than the traffic data which is however outside thescope of this invention.

The converting unit 600 further includes a profile module 600 b adaptedto generate a profile of a user by customising the user profileexpressed in a format typically used by the DME, e.g. with so-called PCA(Principal Component Analysis) values representing the profile of theuser or a centroid representing a typical user of a cluster that theuser is qualified for. The customised labelling information may beexpressed in terms of service levels 0-10, e.g. Messaging services 3,peer-to-peer services 4, etc., or in a more refined descriptive formate.g. “early adopter” or “traditionalist”.

The converting unit 600 further includes a cluster module 600 c adaptedto generate a profile of a cluster of users by customising the clusterprofile basically in the manner described above for the profile module600 b when generating a user profile.

The above-described invention can enable network operators to exportknowledge of end users extracted via Machine learning functions or thelike in the labelling unit to third parties, such as business analystsand vendors, in a more efficient way. Other examples of possible thirdparties are content providers such as IP TV operators, music downloadproviders, or advertisements agencies or the like. The new feature willminimize the need for data mining experts, as explained above, whichwill only be required when configuring the customer specific labellingrules.

While the invention has been described with reference to specificexemplary embodiments, the description is generally only intended toillustrate the inventive concept and should not be taken as limiting thescope of the invention. The invention is defined by the appended claims.

The invention claimed is:
 1. A method of providing labelling informationto a receiving third party regarding one or more terminal users in acommunication network, comprising the following steps executed by alabelling unit connected to a data mining system: receivingcommunication related data generated from executed communications ofsaid one or more terminal users; fetching stored labelling rules whichhave been configured specifically for the third party; converting thereceived communication related data into labelling information, whereina communication habits vector is determined by applying the fetchedlabelling rules on the received communication related data, and thelabelling information is determined for the terminal user(s) based onthe resulting communication habits vector, said labelling informationrepresenting a description of the terminal user(s) with respect to theircommunication habits, wherein the labelling rules are configured bydefining the communication habits vector as a plurality of measurablecommunication habits parameters that correspond to different aspects ofthe communication habits of the terminal user(s), and configuringthresholds for each of the plurality of measurable communication habitsparameters as limits for predefined user labels, classes or categories;generating a profile of the terminal user(s) by customising a user(s)profile expressed in a format used by a Data Mining Engine (DME) fromwhich the communication related data is received; and delivering thedetermined labelling information to the third party, wherein thelabelling information is delivered using a protocol and an interfaceadapted to the third party.
 2. The method according to claim 1, whereinthe delivered labelling information includes a label, category or classof the terminal user(s) as defined by the labelling rules.
 3. The methodaccording to claim 1, wherein the labelling information is describedwith a terminology independent of the underlying traffic types andcommunication techniques.
 4. The method according to claim 1, whereinthe communication habits vector is determined by determining the valuesof the communication habits parameters from the received communicationrelated data, and a user label, class or category is determined based onsaid preconfigured limits for each parameter in the communication habitsvector.
 5. The method according to claim 1, wherein said communicationhabits vector is representative for a single terminal user or a clusterof plural terminal users having similar communication habits.
 6. Themethod according to claim 1, wherein the communication related data isreceived from the DME as processed by one or more Machine LearningAlgorithms (MLAs).
 7. A labelling unit connected to a data mining systemfor providing labelling information to a receiving third party regardingone or more terminal users in a communication network, comprisingcircuitry configured to: receive communication related data generatedfrom executed communications of said one or more terminal users; fetchstored labelling rules which have been configured specifically for thethird party, and to convert the received communication related data intolabelling information, including determining values of parameters in acommunication habits vector by applying the fetched labelling rules onthe received communication related data, and determining labellinginformation for the terminal user(s) based on the communication habitsvector, said labelling information representing a description of theterminal user(s) with respect to their communication habits; store thelabelling rules which have been configured by defining the communicationhabits vector as a plurality of measurable communication habitsparameters that correspond to different aspects of the communicationhabits of the terminal user(s), and configuring thresholds for each ofthe plurality of measurable communication habits parameters as limitsfor predefined user labels, classes or categories; generate a profile ofthe terminal user(s) by customising a user(s) profile expressed in aformat used by a Data Mining Engine (DME) from which the communicationrelated data is received; and deliver the determined labellinginformation to the third party, wherein the labelling information isdelivered using a protocol and an interface adapted to the third party.8. The labelling unit according to claim 7, wherein the deliveredlabelling information includes a label, category or class of theterminal user(s) as defined by the labelling rules.
 9. The labellingunit according to claim 7, wherein the labelling information isdescribed with a terminology independent of the underlying traffic typesand communication techniques.
 10. The labelling unit according to claim7, wherein the circuitry is further configured to determine thecommunication habits vector by determining the values of thecommunication habits parameters from the received communication relateddata, and determining a user label, class or category based on saidpreconfigured limits for each parameter in the communication habitsvector.
 11. The labelling unit according to claim 7, wherein saidcommunication habits vector is representative for a single terminal useror a cluster of plural terminal users having similar communicationhabits.
 12. The labelling unit according to claim 7, wherein thecircuitry is further configured to receive the communication relateddata from the DME as processed by one or more Machine LearningAlgorithms (MLAs).
 13. The labelling unit according to claim 7, whereinthe circuitry is further configured to: create labelling informationrelating to social network relations of the terminal user(s); andgenerate a profile of a cluster of terminal user(s) by customising acluster profile expressed in said format used by the DME.